Aquatic Ecological Flows: Phase 1 Project Report by Rose Hessmiller

APPALACHIAN LANDSCAPE CONSERVATION COOPERATIVE GRANT 2013 MILESTONE REPORT

Grant Number: 2012-03

Grant Title: Development of a hydrologic foundation and flow-ecology relationships for monitoring riverine resources in the Marcellus Shale region

Phase 1 Project Report

Submitted by: William L. Fisher U. S. Geological Survey New York Cooperative Fish and Wildlife Research Unit Cornell University, Ithaca, NY 14853 Contact information: wlf9@cornell.edu, 607-255-2839 Jason Taylor and Maya Weltman-Fahs New York Cooperative Fish and Wildlife Research Unit Cornell University, Ithaca, NY 14853

30 June 2013 (revised 25 July 2013, final 19 August 2013)

Project Narrative: This project provided information on models that predict ecological responses to flow alteration within the Marcellus Shale region of the Appalachian Landscape Conservation Cooperative. The project involves using the Ecological Limits of Hydrologic Alteration (ELOHA) approach to develop a hydrologic foundation, develop flow-ecology relationships, and predict future impacts associated with increased water withdrawals within the Marcellus Shale region. The 1st phase of the project will involve reviewing existing tools and gathering available data within the project area. The 2nd phase of the project will require applying appropriate hydrologic modeling tools to build a hydrologic foundation and estimate flow alteration, followed by relating existing biological data to flow alteration metrics to develop flow-ecology relationships. The hydrologic foundation and flow-ecology relationships will serve as a useful tool for predicting future biological changes associated with increased water withdrawals in the Marcellus Shale region.

Important Background Information: Horizontal hydraulic fracturing has led to rapid expansion of natural gas drilling in the Marcellus Shale deposit in portions of West Virginia and Pennsylvania (see accompanying figure), and is expected to continue and expand into Ohio and New York. Two to seven million gallons of water are needed per hydraulic fracturing â&#x20AC;&#x2DC;stimulationâ&#x20AC;&#x2122; event, a single natural gas well can be fractured several times over its lifespan, and a well pad site can host multiple wells. This large volume of water needed per well, multiplied by the distributed nature of development across the region, suggests that hydraulic fracturing techniques for natural gas development will put substantial strain on regional water supplies (Rahm and Riha 2012). Surface water is the primary source for hydraulic fracturing related water withdrawals in the Susquehanna River basin within the Marcellus Shale region, but groundwater, which has been a major water source in other natural gas deposits, is also a potential water source. Water consumption related to natural gas drilling, whether surface or subsurface, combined with existing concerns over climate change and future non-drilling water resource needs, have sparked concern among hydrologists and aquatic biologists about the sourcing of water within the region. Changes in stream flow may alter available habitat for freshwater biodiversity and other ecological processes in adjacent freshwater ecosystems. This concern highlights the need for the development of region-wide environmental flow policies, including the Marcellus Shale region, that are protective of stream ecosystems well into the future.

Environmental flows can be defined as the flow of water in a natural river or lake that sustains healthy ecosystems and the goods and services that humans derive from them (Poff et al. 1997). A number of measures have proven useful for quantitatively describing the flow of water in a water body: magnitude or the amount of water flowing, in cubic feet per second, or some other unit of measure; duration of a hydrologic condition, such as high or low flow events; timing of

flows; frequency of occurrence; and the rate of change between one type of flow and another. Each of these measures can be characterized by a range of natural variability, with particular emphasis on inter-annual variability. The process of defining environmental flows seeks to preserve enough of the natural variability in these hydrologic measures to protect the ecological functions essential to diverse, healthy communities of aquatic organisms. For example, natural floods are necessary to scour river channels, maintain floodplains, and provide access to floodplains for organisms that depend on them; on the other hand, aquatic biota may not be adversely impacted with some reduction in the natural frequency and duration of flooding. Prescriptions for environmental flows, which seek to balance ecological and economic needs, have been developed for a number of river systems around the globe, including partnerships between the Army Corps of Engineers and The Nature Conservancy for the Savannah River and other rivers.

While river-specific approaches have contributed substantially to the field of flow restoration, the global pace of human modification of river flow regimes, and the growing threat to freshwater biodiversity, demand a framework that can develop flow recommendations for the rivers of an entire region. The Ecological Limits of Hydrologic Alteration (ELOHA) framework seeks to fill this need, beginning with: 1) developing a hydrologic foundation based on modeled baseline and developed hydrographs; 2) classifying stream types using baseline hydrology and geomorphic characteristics to facilitate generalizations that can apply to all the streams within a class; 3) analysis of flow alteration; and 4) development of flow-ecology linkages, which provide testable relationships â&#x20AC;&#x153;that can serve as a starting point for empirically based flow management at a regional scaleâ&#x20AC;? (Poff et al. 2010). This framework incorporates best professional judgment with quantitative analysis of existing data, and has been applied at the watershed level for the Susquehanna, Connecticut, and Potomac rivers, at the statewide level in Massachusetts, Michigan, Maine, and Florida, and efforts are currently underway in the Great Lakes portion of New York and the upper Ohio River region of Pennsylvania.

Goal/Purpose Statement: Flow-ecology hypotheses developed through previous (Ecosystem Flow Recommendations for the Susquehanna River Basin) and current (Upper Ohio/Great Lakes Tributaries) ELOHA projects within or adjacent to the Marcellus Shale region may serve as a framework for developing empirical relationships between hydrologic alteration and ecological responses and making predictions about future scenarios. This will require adequate flow models that can be used to explore flow-ecology relationships to enhance long-term management of aquatic resources across the Marcellus Shale region. Many models have been developed at spatial or temporal scales that do not match existing invertebrate and fish data, model only high or low flows, or were developed by groups who wish to keep them proprietary. Therefore, Phase I of this project will involve an inventory of flow models and the underlying, or potential, data sources from instream monitoring networks to: 1) Determine what ecological flow models that predict both low and high flows and are in use or are applicable to the Marcellus Shale region; and 2) Recommend suitable model(s) for instream flow predictions both dependent and independent of ecological/biological data. In Phase II of this project we will: 3) Apply a predictive model(s) that assesses how existing permitted and non-permitted water uses and future water use will alter critical hydrologic and hydraulic forces that maintain aquatic habitats; and

finally, 4) forecast how biological communities or target species will respond to predicted changes in hydrology.

Specific Deliverables: Phase I Two deliverables were identified for phase I of this project: 1) A report that assesses the availability of hydrologic and ecological flow models suitable for the Marcellus Shale region that predict discharge thresholds and frequency of both high and low flow events and the vulnerabilities these extremes will create for conservation targets, then recommends one or more models for use in the Marcellus Shale region. 2) A georeferenced summary assessment of the adequacy of available ecological data to inform ecological flow model(s) for streams within the Marcellus Shale, including a summary assessment of critical information gaps. We produced these deliverables by accomplishing four objectives: 1) a literature review of hydrologic models currently used within the Marcellus Shale region, 2) development of a georeferenced stream gage database, 3) contact and coordination with users and developers of stream flow modeling tools, and 4) development of a geo-referenced stream biological database for the Marcellus Shale region. Below are the results of our efforts by objective. Review of Hydrologic Models Forty-nine hydrologic flow models were reviewed. Information compiled for each models included: model name, description, landscape generalization, processing style, developing agency, technical contacts, website availability, temporal scale, geographic scale, inputs and outputs. To evaluate the utility of these models for the Marcellus Shale region, we used the following criteria to evaluate each model and tabulated the sum of the individual category rankings to produce an overall ranking for each model. The higher the model rank, the greater utility for this project. When a particular criterion for the model was not available from the literature or documentation for that model, the model received a zero for that category. The zero score reflected the unavailability of information with respect to how a model functions and not the quality of the model. Without that information, the relevance and usefulness of that model for this project would be significantly limited. We thank Brian Buchanan, Cornell University, for developing these model evaluation criteria. Model Criteria Temporal Scale (Monthly=5, Weekly=3, Daily=1) Explanation: Longer time steps (monthly) are preferable to shorter time steps (daily) when modeling hydrologic patterns over large areas (e.g., regions, HUC4-HUC8 drainage basins). Conversely, modeling hydrologic patterns at shorter temporal steps (weekly or daily) are preferable if modeling is conducted over small areas (e.g., subwatersheds, HUC12 drainage basins).

Continuous/Event-based (Continuous=5, Event Based=1). Explanation: Continuous simulation is preferable because modeling will focus on base/low flows and not flood/peak flows. Spatial Scale (Macro=5 [>10,000 km2], Meso=3 [<10,000 km2, 1 km2], Micro=1 [<1 km2]) Explanation: Because we are dealing with a large region (Marcellus Shale), the scale at which the model is applicable must be regional to reliably produce estimates for of different rivers/stream types. Spatial Resolution (Lumped=5, Semi-lumped/Combined lumped/distributed=3, Distributed=1) Explanation: A fully distributed model would be too computationally complicated at the scale of the Marcellus Shale region. Parsimony (Low=1 [â&#x2030;Ľ10 parameters], Medium=3 [10>â&#x2030;¤5], High=5 [<5]) Explanation: Because there are a number of different states and regional agencies collecting data that will need to be used to represent the entire Marcellus Shale region, and consistency is vital across the data for creation of relevant input datasets; therefore, the model chosen must have few input requirements (must be parsimonious). Further, the model must be computationally efficient to run with the large datasets. Inter-agency Collaboration (Low=1 [not currently used by other relevant agencies], High=5 [widely adopted and actively used by other relevant agencies and collaborators]) Explanation: Interagency collaboration is vital to cooperative research, given the size of the region. Code and Model Support Availability (Publically Available/Code is actively maintained/User manuals exist/Website maintained=5, Commercially Available/Some website and code maintenance= 3, Hard to Obtain Research Model/Code is not maintained/Little user support exists =1) Explanation: Model must be updated and supported to be useful, otherwise it will be very difficult to initiate the modeling process and any problems encountered while modeling could be slow down or halt the project. Results Data for each model and the ranks are in the HydroModel-ranked spreadsheet file included with this report. Based on our evaluation of the models, the ABCD monthly water balance model had the highest rank with a score of 33 out of a possible 35. The remaining top ten models that ranked high were: AVGWLF (ArcView Generalized Water Loading Functional model, now known as MapShed) and MIKE 11RR (Rainfall Runoff model) both with a score of 27; GEFC (Global Environmental Flow Calculator), OASIS (Options Analysis in Irrigation Systems) and SWAT (Soil and Water Assessment Tool) all with a score of 25; the TPWBM (Two-Parameter Water Balance Model) with a score of 24; and HEC-HMS (Hydrologic Engineering CenterHydrologic Modeling System), Thornthwaite Monthly Water Balance Model, and WaterFall (Watershed Flow and Allocation system), which all scored 23. Twenty-eight models ranged in

rank between 22 and 11. The KINEROS2 (Kinematic Runoff and Erosion Model v.2) had the lowest score of 5, largely because of missing information. The ABCD model is a monthly water balance model with a continuous processing style that generalizes the landscape. The model runs on a monthly time-step, can be used at a continental to watershed geographic scale, requires climate data (average annual precipitation), potential evapotranspiration (average monthly temperature, solar radiation), streamflow (average monthly) and outputs monthly streamflow. We recommend using this model for modeling flows in rivers and streams in the Marcellus Shale region. This model is applicable across the whole study area and the North Atlantic LCC and the Northeast Climate Science Center projects are currently using it, so it should integrate well across LCC boundaries with other projects. Our model evaluation included a streamflow estimator tool, StreamStats. Although this model type did not rank in the top ten (score equaled 19), primarily be of inadequate information for some criteria, it would be useful for working at small spatial scales such as subwatersheds. We recommend using this model and other similare streamflow estimator tools that have been or are being developed by the U.S. Geological Survey. The USGS PA Water Science Center has developed the Baseline Streamflow Estimator (BaSE) tool (http://pa.water.usgs.gov/projects/surfacewater/flow_estimation/), and the Sustainable Yield Estimator (SYE) tool is currently being developed for New York (Chris Gazoorian, USGS, NY Water Science Center, personal communication). A similar tool was developed for Massachusetts (U.S Geological Society Scientific Investigations Report 2011-5193) Georeferenced Stream Gage Database A database of 187 stream gages in the Marcellus Shale region was provided by Dr. Ryan MacManamay of the Oak Ridge National Laboratory. Dr. MacManamay has completed hydrologic modeling and stream classification research for the Southeast LCC. Information provided for each model includes: location, station ID, station name, drainage area, type, river distance, Water Resources Report notes, screening notes, and stream flow for three time periods: 1900-2009, 1950-2009, 1990-2009. The spreadsheet with the gage information is included in this report. Coordination with Regional Streamflow Modelers and Users During phase 1 we identified and/or made contact with several people in the region who are working with streamflow models and/or ecological (mostly fish) databases. Below is a table of those people and their organizations. Name

Organization

Contacted? Y/N

Comments

Cara Campbell

Research Fish Biologist, U.S. Geological Society, Leetown Science Center, Northern Appalachian Research Laboratory

Met at USGS NARL and discussed fish mapping project.

John Arway

Executive Director, Pennsylvania Fish and Boat Commission

Requested information on PAFBC fish data. Referred us to Leroy Young.

Leroy Young

Pennsylvania Fish and Boat Commission

Refered to Rod Klime

Rod Kime

Pennsylvania Department Y of Environmental Preservation

Inquired about PAFBC data via emails and phone message but got no reply-PAFBC data available through MARIS

Nevin Welte

Western Pennsylvania Conservancy

Did not contact

Tyler Wagner

Assistant Unit LeaderFisheries of the Pennsylvania Cooperative Fish and Wildlife Research Unit

Inquired about PA fish databases.

Dan Cincotta

West Virginia Department of Natural Resources

Did not contact. WV data available through MARIS.

Scott Morrison

West Virginia Department of Natural Resources

Inquired about WV fish databases. Referred us to Dan Cincotta.

Brian Carr

West Virginia Department of Environmental Preservation

Did not contact. WV data available through MARIS.

John Wirts

West Virginia Department of Environmental Preservation

Did not contact. WV data available through MARIS.

Terry Messing

United States Geological Survey â&#x20AC;&#x201C; West Virginia

Did not contact. WV data available through MARIS.

Andy Loftus

Andrew Loftus Consulting and MARIS fish database developer

Had several phone conversations about merit of using MARIS-also provided advice on inporting new data into MARIS

Stuart Welsh

Assistant Unit LeaderFisheries, of the West

Inquired about WV fish databases.

Virginia Cooperative Fish and Wildlife Research Unit

Referred us to Dan Cincotta.

Ruth Thornton

The Nature Conservancy â&#x20AC;&#x201C; West Virginia

Did not contact

Lou Reynolds

Environmental Protection N Agency

Did not contact

Sam Dinkins

ORSANCO

Talked briefly about project at Upper Ohio River environmental flow workshop (TNC)

Arlene Olivero and Mark Anderson

The Nature Conservancy

Multiple phone calls and conference call about stream classification- TNC agreed to incorporate extra area in data layer development for their LCC stream classification project. This stream classification will be available to flow project for stratifying future ecological response models across stream types.

Ryan MacManamay

Oak Ridge National Laboratory

Contacted and had several discussions with Ryan- Ryan is willing to provide his leastaltered gage database for modelling purposes. Ryan is also working on a flow classification for the other APP LCC stream classfication project. Taylor and MacManamay are working on a finer scale Marcellus classification as a side project.

Chris Gazoorian

USGS New York Water Science Center

Met with Chris to discuss the NY streamflow estimator tool.

Stacey Archfield

USGS MA-RI Water Science Center

Did not contact, but work was referenced at AtlLCC and NECSC meeting.

Ben Letcher

USGS Silvio Conte Fish Center and Univ. of Massachusetts

Met at NA and APP LCC aquatics meeting- Discussed potential applicability of ABCD model to

our project and fish database structure Bob Miltner

Ohio EPA

Requested and aquired Ohio EPA fish community data

Doug Carlson

NY Department of Environmental Conservation

Met at NY Sustainable Flow workshops and on multiple 1 on 1 meetings (hosted by TNC and NY COOP)-discussed application of NY data

Fred Henson

NY Department of Environmental Conservation

Met at NY Sustainable Flow workshops and discussed through email conversations (hosted by TNC and NY COOP)-discussed application of NY data

Mark Hartle

Pennsylvania Fish and Boat Commission

Met at Upper Ohio River Environmental Flow workshopsInquired about applicability of PA fish data to environmental flow relationships

Georeferenced Stream Biological Database Summary A fish database representing the four states (NY, PA, WVA, OH) comprising the majority of the Marcellus Shale region was created using available data from state and federal agencies. Data from the Ohio Environmental Protection Agency (OEPA), the United States Geological Survey (USGS) (NAQWA) program, and the United States Environmental Protection Agency (USEPA) Mid-Atlantic EMAP program were reformatted and combined with existing compiled state agency data for NY, PA, and WVA in the Multistate Aquatic Resource Information System (MARIS). We used the MARIS database structure to format all data in an Access database. The fish database will be linked to flow modeling efforts and used to assess flow-ecology relationships in the next phase of this project. The database with the fish information is included in this report. Objectives One of the objectives of phase 1 of this project was to develop a georeferenced summary assessment of the adequacy of available ecological data to inform ecological flow models for streams within the Marcellus Shale region. This involved acquiring georeferenced fish data from multiple agencies who collect data within the Marcellus Shale region (NY, PA, WVA, OH), formatting data to a standard database structure, and assessing the types of data available and the adequacy of different data types for modeling flow ecology relationships in the future.

Database development We used the Multistate Aquatic Resource Information System (MARIS) as a platform for building the Marcellus Shale region fish database. MARIS is a platform hosted by the USGS to share existing state fisheries data across the US. Types of data that can be incorporated into the database include: 

geo-referencing data,



event information including



collection gear



total catch and weight by species

We chose the MARIS platform as a template for the Marcellus Shale region fish database because it provided distinct advantages including: 1. a standardized data template for combining fish data from various sources that has been vetted by the National Fish Habitat Action Plan, 2. a considerable amount of fish data from within the Marcellus Shale region has already been compiled in MARIS, and 3. using the MARIS platform as a database template provides future opportunities for our data acquisition efforts from this project to be incorporated into the online MARIS platform. Currently we are only using the MARIS platform as a database template for this project. The Marcellus Shale Fish database has not been incorporated into the “official” MARIS platform publically available online at http://www.marisdata.org/. However, we consulted with Andy Loftus (MARIS Coordinator) during the database development, and have kept detailed notes of how new data was formatted to load into the MARIS platform (Appendix X), to facilitate potentially moving the database into the larger public MARIS platform in the future. We used the MARIS developer template to combine existing MARIS data (NY, PA, WVA) with additional fish data from state and federal sources. We acquired additional fish data from: 

Ohio collected by OEPA (contact: Bob Miltner, bob.miltner@epa.state.oh.us);



US EPA’s Mid Atlantic Streams Data Sets downloaded from (http://www.epa.gov/emap/html/data/surfwatr/data/index.html); and



USGS NAQWA data downloaded from the USGS BioData website (https://aquatic.biodata.usgs.gov/landing.action).

Several factors had to be addressed for successful incorporation of these datasets into the MARIS platform. Additional taxa, inconsistencies in naming, and non fish vertebrates were assessed against the MARIS species lookup table (tbl_fish_species_lookup). Twenty-two new entries that

comprised primarily new hybrid combinations (20), a subspecies, and an exotic species record were added to the species lookup table from the Ohio dataset. In follow-up conversations with Bob Miltner concerning hybrids, he suggested aggregating hybrids at the genus level. However, to maintain consistency with the MARIS platform, we maintained hybrids, which can always be aggregated later for future analyses. Eight additional taxa were added to the species lookup table from the US EPA dataset. Additionally, 14 taxa comprising miscellaneous records for snakes, turtles, salamanders, frogs, invertebrates and unidentified taxa were removed from the US EPA dataset. Five taxa representing hybrids or higher taxonomic status were added to the species lookup table from the USGS dataset. Fields that corresponded with fields in the MARIS location table template (tbl_location) were identified in the OEPA, USEPA and USGS databases and changed to correct field ids for merging into the MARIS location table. These changes are summarized in Table 1. Some fields were calculated through spatial joins in GIS to update geo-referenced data fields. This process was also performed to update fields and append new data to the tbl_fish_info table in the MARIS platform (Table 2). Tables representing locations, fish records, species info, and dataset and originator ids from each database were appended to a new MARIS template. All new species in the species lookup table were assigned 7 digit maris_fishspecies_id numbers that start with 999 to clearly differentiate them from â&#x20AC;&#x153;officialâ&#x20AC;? MARIS fishspecies ids so that the MARIS folks could decide on numbers later if this dataset is incorporated into MARIS at a later time.

Table 1. Corresponding MARIS and dataset fields for location table Attribute MARIS #

MARIS MARIS MARIS OHIO EPA NY PA WVA

EMAP

NAQUWA

maris_join_id

State

=”OH”

STATE

StateAbb (SiteInfo)

originator_id

=”50”

=”51”

=”52”

dataset_id

=”50”

=”51”

=”52”

maris_water_id

=”OH-OEPA[originator_water_id]”

originator_water_id

RIVERCODE

originator_station_id

STORET

STRM_ID

SiteNumber (SiteInfo)

originator_join_id

water_name

DRAINB (93-96)

station_name

STRMNAME

originator_station_desc

water_type

SITE_TYPE

=”STREAM”

wt_code

Coded based on MARIS #s

coll_loc_type

=”SMALL AREA”

NAME

lat

LATITUDE

LAT_DD

Latitude_dd (SIteInfo)

lon

LONGITUDE

LON_DD

Longitude_dd(SiteInfo)

coord_loc_cd

coll_acc_desc

upstream_lat

upstream_lon

fips_county_maris

fips_state

Added from GIS

StateFIPSCode (SiteInfo)

county_name

Added from GIS

COUNTY

County

cong_dist

Added from GIS

plss_section

plss_township

plss_range

usgs_huc_8

Added

Added from GIS

HUCCODE (SiteInfo)

usgs_huc_10

Added

Added from GIS

usgs_huc_12

HUC

Added from GIS

usgs_huc_10_name

Added from GIS

usgs_huc_12_name

Added from GIS

nhd_reach

nhdplus_v1_reach

nhdplus_v2_reach

originator_fips_county

Added from GIS

CountyFIPSCode (SiteInfo)

comment

X X

Table 2. Corresponding MARIS and dataset fields for fish info table Attrib ute #

MARIS

MARIS NY

MARIS PA

MAR IS WVA

fishinfo_id

maris_join_id

state

originator_id

dataset_id

OHIO EPA

USEPA EMAP

USGS NAWQA

“OH”

STATE

StateAbb (SiteInfo)

“33”

“34”

“52”

“33”

“34”

“52”

originator_water_i X d

Linked from Species lookup table

originator_station _id

STORET

STRM_ID

SiteNumber (FishCount)

originator_join_id

sample_begin_dat e

TDATE

DATE_CO L

CollectionDate (FishCount)

sample_end_date

TDATE

DATE_CO L

CollectionDate (FishCount)

target_species

“ALL”

target_std

“ALL”

maris_fishspecies

Link from species lookup

Link from species

_id 14

originator_species _id

originator_itis_tsn

maris_itis_tsn

originator_sci_na me

te_species_flag

originator_sample _id

Combined originator_samp le_id and sample begin date

gear_type_1

gear_desc_1

table

lookup table

FINCODE

VERTCOD E

PublishedSortOrder (FishCount) IT IS_TSN (FishCOunt)

Linked from Linked Species lookup from table Species lookup table

Linked from Species lookup table

Link to species GENUS + table SPECIES

PublishedTaxonName (FishCount)

Combined X originator_samp le_id and sample begin date

Combined STRM_ID originator_id + and sample VISIT_NO begin date columns to create a unique id code.

SiteVisitSampleNumber (FishCount)

TYPEChanged OEPA codes to MARIS codes

“EL”

Update codes

Added narrative of

“Backpack Electrofishi

Gear (FishMethodANd SubreachInfo)

OEPA ng” sampling types which includes several different Electrofishing methods-These include the original TYPE codes from OEPA 22

gear_type_2

gear_desc_2

sampling_method ology

total_catch

total_weight

X X*

COUNTED TOTAL_WEI GHT (changed from grams to Kilograms)

“Backpack Electrofishi ng”

Pass (FishMethodAndSubrea chInfo)

ABUND

Abundance (FishCount)

Table 2 continued. Attribut e#

MARIS

MARI S NY

MARI S PA

MARI S WVA

OHIO EPA

effort_time

TIME_FISHED

time_units

Changed from seconds to hours

effort_area_dist

DISTANCE_FISHED

area_dist_units

METERS

cpue_time

cpue_space

Calculated as total_catch/effort_area_di st

bpue_time

Calculated as total_weight/effort_time

bpue_space

Calculate as total_weight/effort_area_ dist

pop_est

pop_est_method

pop_est_model

Calculated as total_catch/effort_time

USEP USGS NAWQA A EMAP Seconds Shock Time(FishMethodAndSubreachI nfo)

pop_est_area

pop_est_measure

pop_est_measure_un its

biomass_est

sample_desc

Comment

*not calculated for all samples.

Database structure The Marcellus Shale Fish database consists of five main tables. These tables include: 

tbl_datasets_marcellus: provides info on original dataset and states represented for each dataset_id



tbl_originators_marcellus: provides information on data collecting agency and states representd for each originator_id



tbl_fish_species_lookup_marcellus: provides unique ids (maris_fishspecies_id), common names and scientific names at family, genus and species level (Table 3)



tbl_loc_info_marcellus: provides unique ids for collection sites (originator_station_id) and associated site information, including latitude and longitude, which can be used to link location info to fish collection info in tbl_fish_info_marcellus. Additional queries were run to create refined location tables (Table 4): o tbl_location_marcellus_state_stream_sites: all stream fish collection sites within states or ecoregions that overlap the Marcellus boundary o tbl_location_marcellus_all_sites: all fish collection sites within the Marcellus boundary



tbl_fish_info_marcellus: provides unique ids for each collection event (originator_sample_id) which can be used to link collection information (date, collection methods, effort, species, abundance) with site information in the tbl_loc_info_marcellus table (Table 5).

Information from the last three tables (tbl_fish_species_lookup_marcellus, tbl_location_info_marcellus (or any of the refined location tables), and tbl_fish_info_marcellus) can be combined based on unique ids (highlighted in green in Tables 3-5) and queried based on criteria in the tables (i.e. collection method, targeted sampling verses community sampling, etc.) to develop fish datasets for different analyses in the future.

Table 3. Fields included in the tbl_fish_species_lookup_marcellus table. Green highlighted row is unique id. Field Name

Data Type

Description

maris_fishspecies_id

Number

Unique identifier for tbl_fish_species_ lookup

itis_tsn

Number

Integrated Taxonomic Information System, Taxonomic Serial Number

comm_name

Text

common name

hybrid_genus_spp_group Text

categorization for hybrids, genus, species, or species groups

sci_name

Text

scientific name - genus and species

family

Text

family name

genus

Text

genus name

species

Text

species name

Table 4. Fields included in the tbl_location_info_marcellus table. Green highlighted row is unique id. Field Name

Data Type

Description

state

Text

Mandatory - State Postal Code Abbreviation

Text

If populated, originator's Foreign Key to their sampling locations in tbl_location when coll_loc_type = "ENTIRE"

originator_station_id

Text

If populated, originator's Foreign Key to their sampling locations in tbl_location when coll_loc_type = "SMALL AREA"

originator_join_id

Text

Originator's Foreign Key to their sampling locations in tbl_location

sample_begin_date

Mandatory - Date of the beginning of data collection for Date/Time the sampling event in MM/DD/YYYY format

originator_water_id

sample_end_date

Mandatory - Date of the end of data collection for the Date/Time sampling event in MM/DD/YYYY format

Text

Species or species group code of fish targeted during the sampling effort If the target is unknown, enter "UNKNOWN", if all species are targeted (ie, a fish community estimate), enter "ALL"

target_std

Text

General standardized target group to separate fish community sampling from other sampling ("ALL", "TARGET", or "UNKNOWN")

maris_fishspecies_id

Number

Foreign key to tbl_fish_species_ lookup

originator_species_id

Text

Mandatory - The code used by the originator to designate the species in the database

originator_itis_tsn

Number

Integrated Taxonomic Information System, Taxonomix Serial Number (if provided by originator)

originator_sci_name

Text

The scientific name provided by the originator

originator_sample_id

Text

Sample ID from the originator dataset

Text

FN = Fyke Net, TN=Trap Net, GN=Gill Net, PN=Pound Net, EL=Electrofishing, SE=Seine, TR=Trawl, EP=Eel pot, FP=Fish Pot,CP=Crab Pot, SN=snorkel, HL=Hook & Line, KN=Kick Net, HN = Hoop Net, RO=rotenone, OT=Other

Text

Detailed description of primary gear used in fish collection (originator specific, not standardized)

gear_type_2

Text

gear_desc_2

Text

Detailed description of the secondary gear used in fish collection (originator specific, not standardized)

target_species

gear_type_1 gear_desc_1

sampling_methodology Text

Supplemental information on the method used to sample, such as single pass electrofishing, multi-pass electrofishing, etc Note: if multipass, only the first pass of 22

data for "count" or "CPUE" should be included

Number

Total number of fish caught in sample If species occurrence is noted but not enumerated, this field should be blank

total_weight

Number

Total weight (kilograms) of fish caught in sample for surveys where all fish were weighed If species occurrence is noted but not enumerated, this field should be blank

effort_time

Text

Total duration of sampling effort, not including processing time

Text

Originator standard unit of time for sampling effort (HOURS, DAYS, NETNIGHTS, HAULS)

Field Name

Data Type

Description

effort_area_dist

Number

Total area or distance sampled

Text

originator standard unit of space (area or distance) for sampling effort (METERS, MILES, HECTARES, KILOMETERS, FEET, or ACRES)

Number

Catch Per Unit Effort Time The total number of fish caught per standard unit of time TOTAL_CATCH/EFFORT_TIME for a single gear type DO NOT FILL IN IF GEAR TYPE2 IS POPULATED

Number

Catch Per Unit Effort Space The total number of fish caught per standard unit of space (TOTAL_CATCH/EFFORT_AREA_DIST) for a single gear type DO NOT FILL IN IF GEAR TYPE2 IS POPULATED

total_catch

time_units

Table 4 continued.

area_dist_units

cpue_time

cpue_space

bpue_time

Number

Catch Per Unit Effort Time Biomass The total weight of fish caught per standard unit of time (TOTAL_WEIGHT/EFFORT_TIME) for a single gear type DO NOT FILL IN IF GEAR TYPE2 IS 23

POPULATED

Number

Catch Per Unit Effort Space Biomass The total weight of fish caught per standard unit of space (TOTAL_WEIGHT/EFFORT_AREA_DIST) for a single gear type DO NOT FILL IN IF GEAR TYPE2 IS POPULATED

Number

Population Estimate for sample reach Sampling technique and estimator used is specific to the originator Consult the originator's metadata

Text

Sampling method used to estimate population abundance (SCMR, MCMR, DEP, or OTHER) SCMR = Single Census Mark-Recapture MCMR = Multiple Census MarkRecapture, DEP = Depletion, or OTHER

Text

Population abundance estimator used Choices include CHAPMAN, PETERSON, SCHNABEL, DE LURY, CORMACK JOLLY SEBER, or OTHER

pop_est_area

Text

Population estimate is for the entire waterbody or a smaller area of the waterbody Choices ENTIRE, or SMALL AREA

pop_est_measure

Number

For subsections of the waterbody, linear or areal distance for which the population estimate is measured (eg, 1000)

bpue_space

pop_est

pop_est_method

pop_est_model

pop_est_measure_units Text

Units used to measure POP_EST_MEASURE (METERS, MILES, HECTARES, KILOMETERS, ACRES)

biomass_est

Number

Biomass Estimate for sample reach Sampling technique andmethod is specific to the originator Consult the originator's metadata

sample_desc

Text

A brief description of sampling event

comment

Text

General Comment

Text

Flag provided by originator to indicate species is threatened or endangered, and location information should be withheld from query results

te_species_flag

Table 5. Fields included in the tbl_fish_info_marcellus table. Green highlighted row is unique id and light green rows are unique ids from species lookup and location tables. Field Name

Data Type

Description

state

Text

Mandatory - State Postal Code Abbreviation

Text

If populated, originator's Foreign Key to their sampling locations in tbl_location when coll_loc_type = "ENTIRE"

originator_station_id

Text

If populated, originator's Foreign Key to their sampling locations in tbl_location when coll_loc_type = "SMALL AREA"

originator_join_id

Text

Originator's Foreign Key to their sampling locations in tbl_location

sample_begin_date

Mandatory - Date of the beginning of data collection for Date/Time the sampling event in MM/DD/YYYY format

sample_end_date

Mandatory - Date of the end of data collection for the Date/Time sampling event in MM/DD/YYYY format

originator_water_id

Text

Species or species group code of fish targeted during the sampling effort If the target is unknown, enter "UNKNOWN", if all species are targeted (ie, a fish community estimate), enter "ALL"

target_std

Text

General standardized target group to separate fish community sampling from other sampling ("ALL", "TARGET", or "UNKNOWN")

maris_fishspecies_id

Number

Foreign key to tbl_fish_species_ lookup

originator_species_id

Text

Mandatory - The code used by the originator to designate the species in the database

originator_itis_tsn

Number

Integrated Taxonomic Information System, Taxonomix Serial Number (if provided by originator)

originator_sci_name

Text

The scientific name provided by the originator

originator_sample_id

Text

Sample ID from the originator dataset

gear_type_1

Text

FN = Fyke Net, TN=Trap Net, GN=Gill Net, PN=Pound Net, EL=Electrofishing, SE=Seine, TR=Trawl, EP=Eel 25

target_species

pot, FP=Fish Pot,CP=Crab Pot, SN=snorkel, HL=Hook & Line, KN=Kick Net, HN = Hoop Net, RO=rotenone, OT=Other Text

Detailed description of primary gear used in fish collection (originator specific, not standardized)

gear_type_2

Text

gear_desc_2

Text

Detailed description of the secondary gear used in fish collection (originator specific, not standardized)

gear_desc_1

sampling_methodology Text

Supplemental information on the method used to sample, such as single pass electrofishing, multi-pass electrofishing, etc Note: if multipass, only the first pass of data for "count" or "CPUE" should be included

total_catch

Number

Total number of fish caught in sample If species occurrence is noted but not enumerated, this field should be blank

Number

Total weight (kilograms) of fish caught in sample for surveys where all fish were weighed If species occurrence is noted but not enumerated, this field should be blank

effort_time

Text

Total duration of sampling effort, not including processing time

time_units

Text

Originator standard unit of time for sampling effort (HOURS, DAYS, NETNIGHTS, HAULS)

total_weight

Table 5 continued Field Name

Data Type

Description

effort_area_dist

Number

Total area or distance sampled

Text

originator standard unit of space (area or distance) for sampling effort (METERS, MILES, HECTARES, KILOMETERS, FEET, or ACRES)

Number

Catch Per Unit Effort Time The total number of fish caught per standard unit of time TOTAL_CATCH/EFFORT_TIME for a single gear type DO NOT FILL IN IF GEAR TYPE2 IS POPULATED

Number

Catch Per Unit Effort Space The total number of fish caught per standard unit of space (TOTAL_CATCH/EFFORT_AREA_DIST) for a single gear type DO NOT FILL IN IF GEAR TYPE2 IS POPULATED

Number

Catch Per Unit Effort Time Biomass The total weight of fish caught per standard unit of time (TOTAL_WEIGHT/EFFORT_TIME) for a single gear type DO NOT FILL IN IF GEAR TYPE2 IS POPULATED

Number

Catch Per Unit Effort Space Biomass The total weight of fish caught per standard unit of space (TOTAL_WEIGHT/EFFORT_AREA_DIST) for a single gear type DO NOT FILL IN IF GEAR TYPE2 IS POPULATED

Number

Population Estimate for sample reach Sampling technique and estimator used is specific to the originator Consult the originator's metadata

pop_est_method

Text

Sampling method used to estimate population abundance (SCMR, MCMR, DEP, or OTHER) SCMR = Single Census Mark-Recapture MCMR = Multiple Census MarkRecapture, DEP = Depletion, or OTHER

pop_est_model

Text

area_dist_units

cpue_time

cpue_space

bpue_time

bpue_space

pop_est

Population abundance estimator used Choices include CHAPMAN, PETERSON, SCHNABEL, DE LURY, 27

CORMACK JOLLY SEBER, or OTHER

pop_est_area pop_est_measure

Text

Population estimate is for the entire waterbody or a smaller area of the waterbody Choices ENTIRE, or SMALL AREA

Number

For subsections of the waterbody, linear or areal distance for which the population estimate is measured (eg, 1000)

pop_est_measure_units Text

Units used to measure POP_EST_MEASURE (METERS, MILES, HECTARES, KILOMETERS, ACRES)

biomass_est

Number

Biomass Estimate for sample reach Sampling technique andmethod is specific to the originator Consult the originator's metadata

sample_desc

Text

A brief description of sampling event

comment

Text

General Comment

Text

Flag provided by originator to indicate species is threatened or endangered, and location information should be withheld from query results

te_species_flag

Fish Field Collection Information Summary Sample locations The Marcellus Shale fish database includes existing MARIS fish data for NY (1976-2007), PA (1975-2007), and WVA (1997-2010) with additional data from Ohio EPA (1978-2012), the USEPA EMAP program (1993-1998), and the USGS NAWQA program (1993-2012). There are 35512 locations represented within the database (tbl_location_marcellus_all_sites). There are 14707 unique stream fish collection locations within the Marcellus Shale boundary (tbl_loc_marcellus_fish_locations, Figure 1). In total, there are 437045 fish records within the database (tbl_fish_info_marcellus) with 151151 individual species counts recorded from sites within the Marcellus Shale boundary.

Figure 1. Distribution of sampling site according to source extracted from the Marcellus Shale Fish database that fall within the Marcellus Shale boundary (Maya add source info for boundary here). Unique taxons There were 287 unique Maris fish species ids represented by collections from within the Marcellus Shale region, including new additions from this project (Table 6). These taxa ids represent 27 families and 81 genera of fish. The top 25 most frequently collected taxa in rank order are white suckers, creek chubs, blacknose dace, brown trout, central stonerollers, mottled sculpin, northern hog suckers, smallmouth bass, Johnny darters, brook trout, longnose dace, greenside darters, bluntnose minnows, common shiners, blue gill, fantail darters, rock bass, pumpkinseed, largemouth bass, rainbow darters, cutlips minnows, green sunfish, river chub, common carp, and logperch. Forty-two are hybrids and an additional 26 are higher level taxonomic ids (family or genus). Future development of relationships between flow metrics and fish responses will have to determine the best approach for incorporating hybrid or family/genus data, but this will be dependent on the response measures. Additional taxa may need to be 29

condensed for future analyses (subspecies, different strains). Two darter species not known to occur within the region were extracted from the database. There are 1077 records for the barrens darter from PA. This is likely to be the banded darter and needs to be resolved. Additionally there is a record for the Tennessee darter in the database that must be an error. Table 6. Taxonomic ids extracted from sampling events collected within the Marcellus Shale region. Common name

Genus species

# of records

Bowfin

Amia calva

American eel

Anguilla rostrata

330

Brook silverside

Labidesthes sicculus

167

River carpsucker

Carpiodes carpio

Quillback

Carpiodes cyprinus

436

Highfin carpsucker

Carpiodes velifer

Suckers (Family)

Catostomidae spp.

Longnose sucker

Catostomus catostomus

White sucker

Catostomus commersonii

9193

Suckers (Genus)

Catostomus spp.

Blue sucker

Cycleptus elongatus

Creek chubsucker

Erimyzon oblongus

Lake chubsucker

Erimyzon sucetta

Northern hog sucker

Hypentelium nigricans

5083

Smallmouth buffalo

Ictiobus bubalus

253

Bigmouth buffalo

Ictiobus cyprinellus

Black buffalo

Ictiobus niger

Spotted sucker

Minytrema melanops

162

Silver redhorse

Moxostoma anisurum

586

smallmouth redhorse

Moxostoma breviceps

164

River redhorse

Moxostoma carinatum

138

Black Jumprock

Moxostoma cervinum

Black redhorse

Moxostoma duquesnii

560

Golden redhorse

Moxostoma erythrurum

1714

Shorthead redhorse

Moxostoma macrolepidotum

375

Redhorses

Moxostoma spp.

Greater redhorse

Moxostoma valenciennesi

Torrent Sucker

Thoburnia rhothoeca

Centrarchidae hybrid

Rock bass

Ambloplites rupestris

3109

Unspecified Centrarchid spp.

Centrarchid spp.

Warmouth

Chaenobryttus gulosus

184

Bluespotted sunfish

Enneacanthus gloriosus

Banded sunfish

Enneacanthus obesus

Redbreast sunfish

Lepomis auritus

278

Green sunfish

Lepomis cyanellus

2108

Pumpkinseed

Lepomis gibbosus

2929

Orangespotted sunfish

Lepomis humilis

Green sunfish X Unknown

Lepomis cyanellus x centrarchidae

136

Green sunfish X Pumpkinseed

Lepomis cyanellus x lepomis gibbosus

Green sunfish x Warmouth hybrid

Lepomis cyanellus x lepomis gulosus

Green sunfish X Bluegill

Lepomis cyanellus x lepomis macrochirus

194

Table 6 continued. Common name

Genus species

# of records

Green sunfish X Longear sunfish

Lepomis cyanellus x lepomis megalotis

Pumpkinseed x Warmouth hybrid

Lepomis gibbosus x lepomis gulosus

Pumpkinseed X Orangespotted sunfish

Lepomis gibbosus x lepomis humilis

Pumpkinseed x Bluegill hybrid

Lepomis gibbosus x lepomis macrochirus

Pumpkinseed X Longear sunfish

Lepomis gibbosus x lepomis megalotis

Warmouth x Bluegill hybrid

Lepomis gulosus x lepomis macrochirus

Lepomis hybrids

Bluegill x Orangespotted sunfish hybrid

Lepomis macrochirus x l. humilis

Longear sunfish x Bluegill hybrid

Lepomis megalotis x l. macrochirus

Hybrid Sunfishes

Lepomis spp. (hybrid)

Sunfish hybrid

Lepomis spp. X Lepomis spp.

redbreast x green sunfish

Lepomis auritus x Lepomis cyanellus

Bluegill

Lepomis macrochirus

3266

Longear sunfish

Lepomis megalotis

356

Redear sunfish

Lepomis microlophus

Sunfishes

Lepomis spp.

Smallmouth bass

Micropterus dolomieu

4590

Spotted bass

Micropterus punctulatus

534

Largemouth bass

Micropterus salmoides

2263

White crappie

Pomoxis annularis

431

Pomoxis hybrids

Pomoxis spp. (hybrids)

Black crappie

Pomoxis nigromaculatus

626

Black crappie (blacknose)

Pomoxis nigromaculatus (blacknose)

Blueback herring

Alosa aestivalis

Skipjack herring

Alosa chrysochloris

Alewife

Alosa pseudoharengus

American shad

Alosa sapidissima

Herrings

Alosa spp.

Gizzard shad

Dorosoma cepedianum

1043

Oriental weatherfish

Misgurnus anguillicaudatus

Mottled sculpin

Cottus bairdii

5116

Blue Ridge sculpin

Cottus caeruleomentum

Banded sculpin

Cottus carolinae

Slimy sculpin

Cottus cognatus

1366

Potomac sculpin

Cottus girardi

Kanawha Scuplin

Cottus kanawhae

Checkered Sculpin

Cottus n. sp.

Sculpins

Cottus spp.

584

Hybrid x Minnow

Minnow hybrid

Central stoneroller

Campostoma anomalum

6087

Goldfish

Carassius auratus

119

Redside dace

Clinostomus elongatus

1115

Rosyside dace

Clinostomus funduloides

Redside dace X Creek chub

Clinostomus elongatus x semotilus 33

atromaculatus Redside Dace x Striped Shiner

Clinostomus elongatus x luxilus chrysocephalus

Lake chub

Couesius plumbeus

Common name

Genus species

# of records

Grass carp

Ctenopharyngodon idella

Triploid grass carp

Ctenopharyngodon idella (triploid)

Satinfin shiner

Cyprinella analostana

Whitetail shiner

Cyprinella galactura

Cyprinella Hybrid

Cyprinella Sp. x Cyprinella Sp.

Spotfin shiner

Cyprinella spiloptera

1240

Satinfin shiners

Cyprinella spp.

Steelcolor shiner

Cyprinella whipplei

Undetermined CYPRINID

Cyprinidae spp. (Undetermined)

Sheepshead minnow

Cyprinodon variegatus

Common carp

Cyprinus carpio

1908

Carp x Goldfish hybrid

Cyprinus carpio x carassius auratus

Streamline chub

Erimystax dissimilis

Gravel chub

Erimystax x-punctatus

Tonguetied minnow

Exoglossum laurae

107

Cutlips minnow

Exoglossum maxillingua

2222

Brassy minnow

Hybognathus hankinsoni

Eastern silvery minnow

Hybognathus regius

Table 6 continued.

Bigeye chub

Hybopsis amblops

White Shiner

Luxilus albeolus

Crescent Shiner

Luxilus cerasinus

Striped shiner

Luxilus chrysocephalus

1302

Common shiner

Luxilus cornutus

3303

Common shiner X Striped shiner

Luxilus cornutus x luxilus chrysocephalus

Striped shiner X River chub

Luxilus chrysocephalus x nocomis micropogon

Striped Shiner x Rosyface Shiner

Luxilus chrysocephalus x notropis rubellus

Striped Shiner x Creek Chub

Luxilus chrysocephalus x semotilus atromaculatus

Striped Shiner x Stoneroller

Luxilus chrysocephalus x campostoma anomalum

Striped Shiner x Silver Shiner

Luxilus chrysocephalus x notropis photogenis

Striped Shiner x Southern Redbelly Dace

Luxilus chrysocephalus x phoxinus erythrogaster

Striped Shiner x Redfin Shiner

Luxilus chrysocephalus x lythrurus umbratilis

highscale shiners

Luxilus spp.

Rosefin shiner

Lythrurus ardens

Scarletfin shiner

Lythrurus fasciolaris

Redfin shiner

Lythrurus umbratilis

133

Silver chub

Macrhybopsis storeriana

118

Pearl dace

Margariscus margarita

249

Hornyhead chub

Nocomis biguttatus

River Chub x Stoneroller

Nocomis micropogon x campostoma anomalum

Bluehead chub

Nocomis leptocephalus

River chub

Nocomis micropogon

2103

Bigmouth Chub

Nocomis platyrhynchus

Golden shiner

Notemigonus crysoleucas

833

Comely shiner

Notropis amoenus

Popeye shiner

Notropis ariommus

Emerald shiner

Notropis atherinoides

802

Common name

Genus species

# of records

Bridle shiner

Notropis bifrenatus

River shiner

Notropis blennius

Silverjaw minnow

Notropis buccatus

993

Ghost shiner

Notropis buchanani

Ironcolor shiner

Notropis chalybaeus

Bigmouth shiner

Notropis dorsalis

Blackchin shiner

Notropis heterodon

Blacknose shiner

Notropis heterolepis

Spottail shiner

Notropis hudsonius

685

Pugnose shiner X Blackchin shiner

Notropis anogenus x notropis heterodon

Rosyface Shiner x Silver Shiner

Notropis rubellus x notropis photogenis

Table 6 continued.

Sand Shiner x Silver Shiner

Notropis stramineus x notropis photogenis

Silver shiner

Notropis photogenis

659

Swallowtail shiner

Notropis procne

Rosyface shiner

Notropis rubellus

1519

New River Shiner

Notropis scabriceps

subspecies of mimic shiner

Notropis sp cf volucellus

Eastern shiners

Notropis spp. (Eastern shiners)

Sand shiner

Notropis stramineus

851

Telescope shiner

Notropis telescopus

Mimic shiner

Notropis volucellus

498

Channel shiner

Notropis wickliffi

Suckermouth minnow

Phenacobius mirabilis

Kanawha Minnow

Phenacobius teretulus

Blackside dace

Phoxinus cumberlandensis

Northern redbelly dace

Phoxinus eos

Southern redbelly dace

Phoxinus erythrogaster

218

mountain redbelly dace

Phoxinus oreas

Bluntnose minnow

Pimephales notatus

3712

Fathead minnow

Pimephales promelas

375

Bullhead minnow

Pimephales vigilax

Blacknose dace

Rhinichthys atratulus

7127

Longnose dace

Rhinichthys cataractae

4223

Blacknose Dace Hybrid (Rhinichthys atratulus x R.obtusus)

Rhinichthys atratulus x R. obtusus

Western blacknose dace

Rhinichthys obtusus

1065

Rudd

Scardinius erythrophthalmus

Creek chub

Semotilus atromaculatus

7652

Fallfish

Semotilus corporalis

1265

Creek chubs

Semotilus spp.

Grass pickerel

Esox americanus

Redfin pickerel (subspecies)

Esox americanus americanus

472

Grass pickerel (vermiculatus)

Esox americanus vermiculatus

Northern pike X Grass pickerel Esox lucius x esox americanus

Tiger muskellunge

Esox lucius x esox masquinongy

Northern pike

Esox lucius

177

Muskellunge

Esox masquinongy

147

Common name

Genus species

# of records

Chain pickerel

Esox niger

438

Pikes

Esox spp.

killifishes and Topminnows

Fundulidae spp.

Banded killifish

Fundulus diaphanus

Western Banded Killifish (subspecies)

Fundulus diaphanus menona

Mummichog

Fundulus heteroclitus

Blackstripe topminnow

Fundulus notatus

Burbot

Lota lota

Brook stickleback

Culaea inconstans

Table 6 continued.

Grand Total

151151

Goldeye

Hiodon alosoides

Mooneyes

Hiodon spp.

Mooneye

Hiodon tergisus

White catfish

Ameiurus catus

Black bullhead

Ameiurus melas

120

Yellow bullhead

Ameiurus natalis

1341

Brown bullhead

Ameiurus nebulosus

1074

Channel catfish

Ictalurus punctatus

845

Catfishes

Ictalurus spp.

Mountain madtom

Noturus eleutherus

Slender madtom

Noturus exilis

Yellowfin madtom

Noturus flavipinnis

Stonecat

Noturus flavus

652

Tadpole madtom

Noturus gyrinus

Margined madtom

Noturus insignis

275

Brindled madtom

Noturus miurus

Madtoms

Noturus spp.

Flathead catfish

Pylodictis olivaris

233

Alligator gar

Atractosteus spatula

348

Spotted gar

Lepisosteus oculatus

Longnose gar

Lepisosteus osseus

149

White perch

Morone americana

White bass

Morone chrysops

388

Morone saxatilis x Morone americana

Hybrid Striped Bass - Female

sbass x male wperch Striped bass x White bass hybrid (Wiper)

Morone saxatilis x morone chrysops

109

Striped bass

Morone saxatilis

Rainbow smelt

Osmerus mordax

Greenside darter

Etheostoma blennioides

3799

Rainbow darter

Etheostoma caeruleum

2254

Bluebreast darter

Etheostoma camurum

Fantail darter

Etheostoma flabellare

3144

Barrens darter

Etheostoma forbesi

1077

Spotted darter

Etheostoma maculatum

Johnny darter

Etheostoma nigrum

4576

Tessellated darter

Etheostoma olmstedi

1785

Common name

Genus species

# of records

Candy Darter

Etheostoma osburni

Eastern sand darter

Etheostoma pellucidum

Orangethroat darter

Etheostoma spectabile

Etheostoma

Etheostoma spp.

Tennessee darter

Etheostoma tennesseense

Tippecanoe darter

Etheostoma tippecanoe

Variegate darter

Etheostoma variatum

512

Banded darter

Etheostoma zonale

1280

Yellow perch

Perca flavescens

977

Table 6 continued.

Logperch

Percina caprodes

1867

Channel darter

Percina copelandi

Gilt darter

Percina evides

Appalachia Darter

Percina gymnocephala

Longhead darter

Percina macrocephala

Blackside darter

Percina maculata

981

Sharpnose darter

Percina oxyrhyncha

Shield darter

Percina peltata

416

Slenderhead darter

Percina phoxocephala

Roanoke Darter

Percina roanoka

Dusky darter

Percina sciera

River darter

Percina shumardi

Roughbelly darters

Percina spp.

Sauger

Sander canadensis

636

Walleye X Sauger (Saugeye)

Sander vitreus x sander canadensis

Walleyes and Saugers

Sander spp.

102

Walleye

Sander vitreus

977

Trout-perch

Percopsis omiscomaycus

245

Ohio lamprey

Ichthyomyzon bdellium

Northern brook lamprey

Ichthyomyzon fossor

Northern brook lamprey (ammocoete)

Ichthyomyzon fossor (ammocoete)

Mountain brook lamprey

Ichthyomyzon greeleyi

Lampreys

Ichthyomyzon spp.

Lampreys (ammocoetes 1)

Ichthyomyzon spp. (ammocoetes 1)

Least brook lamprey

Lampetra aepyptera

121

American brook lamprey

Lampetra appendix

American brook lamprey (ammocoete)

Lampetra appendix (ammocoete)

Lampetra

Lampetra spp.

Sea lamprey (ammocoete)

Petromyzon marinus (ammocoete)

Sea lamprey

Petromyzon marinus

Unidentified lamprey

Petromyzontidae spp.

Western Mosquitofish

Gambusia affinis

Paddlefish

Polyodon spathula

Coho salmon

Oncorhynchus kisutch

Rainbow trout

Oncorhynchus mykiss

811

Rainbow trout (strain 3)

Oncorhynchus mykiss (strain 3)

403

Rainbow trout (strain 2)

Oncorhynchus mykiss (strain 2)

Table 6 continued. Common name

Genus species

# of records

Rainbow trout (strain 1)

Oncorhynchus mykiss (strain 1)

Rainbow trout (domestic)

Oncorhynchus mykiss (domestic)

590

Atlantic salmon

Salmo salar

Brown trout (domestic)

Salmo trutta (domestic)

732

Brown trout

Salmo trutta

6109

trout hybrid

Salmonidae spp.

Brook trout (domestic)

Salvelinus fontinalis (domestic)

1419

Brook trout

Salvelinus fontinalis

4370

Tiger trout

Salvelinus fontinalis x salmo trutta

Lake trout

Salvelinus namaycush

Trouts

Salvelinus and salmo spp.

Freshwater drum

Aplodinotus grunniens

649

Central mudminnow

Umbra limi

237

Eastern mudminnow

Umbra pygmaea

UNKNOWN

No fish collected.

107

Sampling events The distribution of fish sampling events varies across states with NY, PA and OH having significantly more sampling effort than WVA. A few sampling events from federal programs occurred in MD and VA but state agency data has not been included in the database at this time. Fish data is collected for a variety of reasons using different collection techniques. Collection purposes can range from surveys for rare or endangered species, general fish species distributions, calculating water quality indices such as the index of biotic integrity (IBI), and monitoring and managing sport fisheries. Within the Marcellus Shale fish database there are two 43

fields that can be used to sort or select collection records by purpose (target_std) and method (gear_type_1). In general, targe_std is broken up into three categories, ALL, TARGET, or UNKNOWN. TARGET surveys represent collection events focused on a particular species or group of gamefish (trout, smallmouth bass). When the purpose of the survey was to describe the assemblage, it is assigned as ALL. UNKNOWN is self explanatory but can be assumed to represent general fish assemblage surveys but may not have been conducted with standard methods. Fish sampling recorded within the database was conducted with several types of sampling gear, with the dominant gear type being electro-fishing (Table 7). However, all electrofishing efforts are not equal. New York and PA only use standard methods for conducting targeted game fish surveys (Table 7) (personal communication with Doug Carlson (dmcarlso@gw.dec.state.ny.us), Fred Henson (fghenson@gw.dec.state.ny.us), and Mark Hartle (mhartle@state.pa.us)). Thus targeted electro-fishing sampling events within these states may provide adequate abundance estimates for target species (trout), but not necessarily the whole fish assemblage. In contrast, Ohio EPA, WV DEP, USEPA and USGS collect fish with comparable standard electro-fishing methods designed to collect data for calculating biotic indices and can also be used to describe assemblage structure based on relative abundance estimates or presence/absence data (Table 8). Additionally, non target data from NY and PA will provide reliable presence/absence data which combined with the other data sources provides a considerable amount of data for developing individual species occupancy models or measures of assemblage structure or functional traits based on presence/absence data (Table 8).

Table 7. Distribution of sampling events within the Marcellus Shale boundary by agency, collection type, and collection method for each state. EL = electrofishing, GN = gillnet, OT=, SE = seinge, TN = trapnet, HL = , UN = unknown. Rows highlighted in green represent sample types that provide community information. Rows highlighted in orange represent targeted game fish sampling (primarily trout). MD

WVA

Total

NYDEC

2960

All

1663

1414

118

Target

1297

1272

PAFABC

9790

All

Target

5060

Unknown

4720

4219

258

150

WVADEP

196

All

Target

Unknown

OEPA

2221

All

2221

2219

USEPA

154

104

293

All

154

104

293

154

104

293

USGS

All

Total

2992

2221

9969

307

15502

Table 8. Distribution of non-targeted electro-fishing sampling events within the Marcellus Shale boundary by agency for each state. Number of sampling events that can be used to describe community structure or individual species occupancy based on presence/absence are summarized in P-A Data row. Number of sampling events that may be used for computing biotic indices or describing changes in community structure based on relative abundance are summarized in Rel Abd Data row. MD

WVA

Total

NYDEC All-EL

1414*

1414

PAFABC Unknown-EL

4219*

4219

WVDEP All-EL

Unknown-EL

OEPA All-EL

2219

USEPA All-EL

154

104

293

2219

4398

211

8287

2219

179

USGS All-EL

P-A Data

Rel Abd Data

1446 32

211 2654

*Not necessarily collected with standard methods.

Potential Applications and Limitations The large number of sampling events available for analyses should provide opportunities for several different kinds of analyses that relate fish response to modeled flow metrics. First target electro-fishing data can be used to relate trout abundance to landscape and local habitat, including modeled flow metrics. Additionally, this data can be combined with other data to model occupancy instead of abundance. Second, fish based response metrics or ecological trait measures (% fluvial fish) can be calculated from available relative abundance data (OEPA, WVDEP, USEPA, USGS) collected for biomonitoring purposes. This data may also be useful for conducting multivariate analysis (nMDS ordinations) to relate assemblage structure to modeled flow metrics across a range of sites. This dataset is not without limitations including unequal distribution of sampling effort, a diversity of sampling methods, and differences in taxonomic resolution among sampling events. Unequal distribution of sampling effort among states is a common problem in trans-boundary datasets. Overcoming this may require taking random subsets of sampling events from data rich regions to even out the spatial distribution of data. Acquiring additional data may help fill in data sparse areas. Data from Maryland and Virginia state agencies have not been incorporated into this database and may provide additiona data in the small portions of the Marcellus Shale region that these states occupy. There is more fish data present in WVA, however WV Dept. of Natural Resources is not willing to release fish data to open source databases (Kauffman 2012). Additional sources of brook trout data (Trout Unlimited) could be added to strengthen brook trout datasets. While there are a diversity of sampling methods present in the dataset, electrofishing is by far the dominant gear used to collect fish in the region. Limiting datasets to those that use electro-fishing gear should not limit available data very much. However, limiting datasets to those that electro-fish with standard methods for calculating biotic indices limits the amount of data substantially. Still, there are enough sampling events (2654) that even with subsetting Ohio data, should result in a fairly substantial dataset (~300-500 events) for the region. Additionally, this data may be augmented with new data from the recently released US EPA National Stream Survey. There are 244 NSS sites within states that overlap the Marcellus Shale boundary (MD, NY, OH, PA, VA, WVA). Sites that fall directly within the boundary should be extracted and added to the database. Differences in taxonomic resolution are also a common problem in databases representing a wide range of agencies, methods, and collecting abilities. Overcoming differences in taxonomic resolution will require making tough choices about removing taxa, or sites with taxa not identified to species, or splitting counts of unidentified taxa across abundances of taxa within the same classification (genus). Despite these limitations, the database presented in this report should provide a useful tool for developing flow-ecology models when combined with future hydrologic modeling efforts in phase 2 of this project.

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